平均背景建模法

have one camera, but this routine allows
//
you to index many.
//
#ifndef AVGSEG_
#define AVGSEG_
#include "cv.h" #include "highgui.h" #include "cxcore.h"
// define all of the opencv classes etc.
#endif
// avg_background.cpp : 定义控制台应用程序的入口点。 //
#include "stdafx.h" #include "avg_background.h"
//GLOBALS
IplImage
*IavgF[NUM_CAMERAS],*IdiffF[NUM_CAMERAS],
work.
////Then continue detecting foreground in the mask image
// backgroundDiff(IplImage *I,IplImage *Imask, int num);
//
//NOTES: num is camera number which varies from 0 ... NUM_CAMERAS - 1. Typically you only
*IprevF[NUM_CAMERAS],
*IhiF[NUM_CAMERAS], *IlowF[NUM_CAMERAS];
IplImage *Iscratch,*Iscratch2,*Igray1,*Igray2,*Igray3,*Imaskt;
IplImage
*Ilow1[NUM_CAMERAS],*Ilow2[NUM_CAMERAS],*Ilow3[NUM_CAMERAS],*Ihi1[NUM_CAMERA
Ilow1[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 ); Ilow2[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 ); Ilow3[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 ); Ihi1[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 ); Ihi2[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 ); Ihi3[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 ); cvZero(IavgF[i] ); cvZero(IdiffF[i] ); cvZero(IprevF[i] ); cvZero(IhiF[i] ); cvZero(IlowF[i] ); Icount[i] = 0.00001; //Protect against divide by zero } Iscratch = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 3 ); Iscratch2 = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 3 ); Igray1 = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 ); Igray2 = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 ); Igray3 = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 ); Imaskt = cvCreateImage( cvGetSize(I), IPL_DEPTH_8U, 1 );
// Accumulate the background statistics for one more frame
// We accumulate the images, the image differences and the count of images for the
// the routine createModelsfromStats() to work on after we're done accumulating N frames.
background and foreground
//
// Typical way of using this is to:
// AllocateImages();
////loop for N images to accumulate background differences
// accumulateBackground();
//This function can handle an array of cameras //How many average differences from average
//How many average differences from average image on
void AllocateImages(IplImage *I); void DeallocateImages(); void accumulateBackground(IplImage *I, int number=0); void scaleHigh(float scale = HIGH_SCALE_NUM, int num = 0); void scaleLow(float scale = LOW_SCALE_NUM, int num = 0); void createModelsfromStats(); void backgroundDiff(IplImage *I,IplImage *Imask, int num = 0);
// I
Background image, 3 channel, 8u
// number Camera number
void accumulateBackground(IplImage *I, int number)
{
static int first = 1;
cvCvtScale(I,Iscratch,1,0); //To float;#define cvCvtScale cvConvertScale #define cvScale
cvZero(Iscratch); cvZero(Iscratch2 ); }
void DeallocateImages() {
for(int i=0; i<NUM_CAMERAS; i++){ cvReleaseImage(&IavgF[i]); cvReleaseImage(&IdiffF[i] ); cvReleaseImage(&IprevF[i] ); cvReleaseImage(&IhiF[i] ); cvReleaseImage(&IlowF[i] ); cvReleaseImage(&Ilow1[i] ); cvReleaseImage(&Ilow2[i] ); cvReleaseImage(&Ilow3[i] ); cvReleaseImage(&Ihi1[i] ); cvReleaseImage(&Ihi2[i] ); cvReleaseImage(&Ihi3[i] );
avg_background.h 文件：
//////////////////////////////////////////////////////////////////////////////////////////////////////
/////////////
// Accumulate average and ~std (really absolute difference) image and use this to detect
average differences below average
// scaleHigh(scalehigh);
// scaleLow(scalelow);
////That is, change the scale high and low bounds for what should be background to make it
////When done, turn this into our avg and std model with high and low bounds
// createModelsfromStats();
////Then use the function to return background in a mask (255 == foreground, 0 == background)
// backgroundDiff(IplImage *I,IplImage *Imask, int num);
////Then tune the high and low difference from average image background acceptance
thresholds
// float scalehigh,scalelow; //Set these, defaults are 7 and 6. Note: scalelow is how many
cvConvertScale
if (!fiavgF[number]);// 将 2 幅 图 像 相 加 ：
IavgF[number]=IavgF[number]+Iscratch，IavgF[]里面装的是时间序列图片的累加